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Nisum Increases Forecast Accuracy by 25% using an Artificial Intelligence Driven Forecasting Framework

Nov 30, 2020 10:27:52 AM

Nisum developed a framework utilizing an AI-driven approach to improve forecast accuracy.



The client has seen improvement in forecast accuracy, resulting in:

  • 25% reduction in execution time for forecast processing
  • 25% increase in accuracy for accurate order delivery date (AODD)


Business Challenge:

A Fortune 500 premium goods retailer had a manual process for forecasting orders which provided an inaccurate quantity of orders, leading to:

  • Poor resource planning due to inaccurate forecasts, leading to:
    • The inability to meet peak holiday capacity 
  • Inaccurate order delivery dates due to unavailable seasonal forecasts



Nisum developed a framework for effective resource optimization using KPIs such as Site Availability, Service Violations, Shift Coverage. Nisum also used an AI-driven approach to handle data; a high accuracy iterative model was developed, tested, and fitted, leading to:

  • Faster forecast processing with improved downtime tracking and enhanced availability across the ecosystem by consolidating historical data as well as additional information for effective forecasting.
    • Using Exploratory Data Analysis (EDA) for missing values and outlier treatment
  • Improved AODD with improved forecasting of resources using feature engineering. They derived features with impact on response variables to create trend, seasonal, and cyclic variations of forecasts for optimal simulations.
  • Increased forecast accuracy by tracking forecast accuracy and model performance periodically using accuracy metrics such as MAPE, MSE, RMSE, and R-Square.

Feel free to contact us for more information on how Nisum can drive results for your company.


Written by Nisum

Founded in California in 2000, Nisum is a digital commerce company focused on strategic IT initiatives using integrated solutions that deliver real and measurable growth.